tf.compat.v1.nn.fractional_max

Batch normalization.

tf.compat.v1.nn.fused_batch_norm(
    x, scale, offset, mean=None, variance=None, epsilon=0.001,
    data_format='NHWC', is_training=True, name=None,
    exponential_avg_factor=1.0
)

See Source: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift; S. Ioffe, C. Szegedy.

Args
`x`	Input `Tensor` of 4 or 5 dimensions.
`scale`	A `Tensor` of 1 dimension for scaling.
`offset`	A `Tensor` of 1 dimension for bias.
`mean`	A `Tensor` of 1 dimension for population mean. The shape and meaning of this argument depends on the value of is_training and exponential_avg_factor as follows: is_trainingFalse (inference): Mean must be a `Tensor` of the same shape as scale containing the estimated population mean computed during training. is_trainingTrue and exponential_avg_factor == 1.0: Mean must be None. is_trainingTrue and exponential_avg_factor != 1.0: Mean must be a `Tensor` of the same shape as scale containing the exponential running mean.
`variance`	A `Tensor` of 1 dimension for population variance. The shape and meaning of this argument depends on the value of is_training and exponential_avg_factor as follows: is_trainingFalse (inference): Variance must be a `Tensor` of the same shape as scale containing the estimated population variance computed during training. is_training==True and exponential_avg_factor == 1.0: Variance must be None. is_training==True and exponential_avg_factor != 1.0: Variance must be a `Tensor` of the same shape as scale containing the exponential running variance.
`epsilon`	A small float number added to the variance of x.
`data_format`	The data format for x. Support "NHWC" (default) or "NCHW" for 4D tenors and "NDHWC" or "NCDHW" for 5D tensors.
`is_training`	A bool value to specify if the operation is used for training or inference.
`name`	A name for this operation (optional).
`exponential_avg_factor`	A float number (usually between 0 and 1) used for controlling the decay of the running population average of mean and variance. If set to 1.0, the current batch average is returned.

Returns
`y`	A 4D or 5D Tensor for the normalized, scaled, offsetted x.
`running_mean`	A 1D Tensor for the exponential running mean of x. The output value is (1 - exponential_avg_factor) * mean + exponential_avg_factor * batch_mean), where batch_mean is the mean of the current batch in x.
`running_var`	A 1D Tensor for the exponential running variance The output value is (1 - exponential_avg_factor) * variance + exponential_avg_factor * batch_variance), where batch_variance is the variance of the current batch in x.

References:

Batch Normalization - Accelerating Deep Network Training by Reducing Internal Covariate Shift: Ioffe et al., 2015 (pdf)

TensorFlow

tf.compat / v1 / v1.nn.fractional_max_pool

Args

Returns

References:

TensorFlow

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tf.compat / v1 / v1.nn.fractional_max_pool

Args

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References: